Growth and Risk at the Industry Level: the Real Effects ofFinancial Liberalization∗

Andrei A. LevchenkoUniversity of Chicago GSB and IMF

Romain RancièreIMF, PSE, and CEPR

Mathias ThoenigUniversité de Genève, PSE, and CEPR

May 31, 2008

Abstract

This paper analyzes the effects of financial liberalization on growth and volatility atthe industry level in a large sample of countries. We estimate the impact of liberalizationon production, employment, firm entry, capital accumulation, and productivity. In orderto overcome omitted variables concerns, we employ a number of alternative difference-in-differences estimation strategies. We implement a propensity score matching algorithmto find a control group for each liberalizing country. In addition, we exploit variation inindustry characteristics to obtain an alternative set of difference-in-differences estimates.Financial liberalization is found to have a positive effect on both growth and volatility ofproduction across industries. The positive growth effect comes from increased entry offirms, higher capital accumulation, and an expansion in total employment. By contrast,we do not detect any effect of financial liberalization on measured productivity. Finally,the growth effects of liberalization appear temporary rather than permanent.JEL Classification Codes: F02, F21, F36, F4.Keywords: financial liberalization, growth, volatility, industry-level data, difference-

in-differences estimation, propensity score matching.

∗We are grateful to Mathieu Taschereau-Dumouchel for superb research assistance, and to the editors,two anonymous referees, Jean Imbs, Olivier Jeanne, Raphael Lam, Gilles Saint-Paul, Aaron Tornell, ThierryTressel, Thijs van Rens, and workshop participants at the IMF, Université de Genève, HEC-Lausanne,UCLA, PSE, INSEAD, HEC-Paris, UC Santa Cruz, IADB, ESSIM (Izmir), and CREI/CEPR Conference onFinance, Growth and The Structure of the Economy (Barcelona) for helpful comments. The views expressedin this paper are those of the authors and should not be attributed to the International Monetary Fund, itsExecutive Board, or its management. Correspondence: International Monetary Fund, 700 19th Street NW,Washington, DC, 20431, USA. E-mail: alevchenko@imf.org, rranciere@imf.org, thoenig@ecopo.unige.ch.The supplementary appendix is available at http://alevchenko.com/LRT_web_appendix.pdf.

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1 Introduction

Financial markets have been liberalized dramatically in many countries over the past three

decades. Figure 1 depicts recent trends in the indicators of financial openness. Most de

jure measures of restrictions on domestic capital allocation or international capital flows

show a strong trend towards liberalization. Indeed, capital flows across borders have corre-

spondingly grown at a higher pace than the expansion of goods trade, and much faster than

GDP. What are the effects of financial liberalization? In spite of a theoretical case that

financial liberalization should improve the allocation of capital and increase growth, the

growth effects of financial liberalization have not been easy to demonstrate in cross-country

data. At the same time, worries persist that financial liberalization may result in higher

volatility.1

This paper examines the relationship between financial liberalization, growth, and volatil-

ity using a large industry-level panel dataset. The empirical analysis answers three sets of

questions. First, what is the impact of financial liberalization on output growth and volatil-

ity at the industry level? Both growth and volatility effects have been analyzed separately

in cross-country data. However, to obtain a reliable estimate of the their relative impor-

tance it is essential to consider these effects within a unified empirical framework. Second,

what are the channels through which financial liberalization affects growth? And third, are

the effects of financial liberalization permanent or temporary? The answers to the last two

questions shed light on the nature of the relationship between liberalization and growth,

and can help distinguish between the different theoretical possibilities.

The main findings can be summarized as follows. Financial liberalization increases both

growth and volatility of output. These effects are robust to a variety of specifications and

estimation strategies. The growth effect is driven by higher employment, greater capital

accumulation, and greater firm entry. By contrast, we do not detect any impact of liberal-

ization on TFP growth. Finally, the growth impact is temporary rather than permanent:

for output, firm entry, and employment, the effect decreases in magnitude over time, and

becomes insignificant after 6 years, while the impact on capital accumulation is slightly

more long-lasting. The only persistent effect is on competition: the impact of financial

liberalization on the price-cost margin — a measure of markups — increases progressively

for the first few post-liberalization years, and remains significantly negative throughout the

period we analyze. We conclude that financial liberalization has a permanent effect on the

1Kose, Prasad, Rogoff, and Wei (2006) provide a comprehensive exposition of basic facts about the currentwave of financial globalization, and review existing literature on its growth and volatility effects.

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level of output, but no persistent effect on output growth. While the effect of financial liber-

alization on volatility is also most pronounced on impact, we cannot rule out the possibility

of a permanent increase in variance of output growth.

When it comes to interpreting these results, it is useful to consider the range of theo-

retical possibilities for the growth benefits associated with financial liberalization. At one

extreme, in a standard deterministic neoclassical framework, capital mobility accelerates

convergence but has no long-run effect on growth or the level of income.2 At the other

extreme, in an endogenous growth framework risk-diversification and specialization in more

efficient technologies can have permanent growth-enhancing effects.3 Our findings of a per-

manent level effect but no persistent growth effect seem to reject either of these two polar

views. However, they are consistent with the notion that capital mobility raises produc-

tion efficiency by reducing domestic distortions.4 In particular, our empirical results can be

rationalized within a neoclassical model with imperfect competition. In such a model, a per-

manent reduction in markups leads to a temporary growth increase reflecting convergence

towards higher levels of capital and income.5

Until recently, most of the empirical literature studying financial liberalization used

country-level data, and as a result was subject to both conceptual and econometric problems.

First, conceptually, if financial markets are not perfect within the country, the economy

does not behave like a representative agent. Indeed, there is strong evidence that risk

sharing between agents within a country is far from complete even in the most advanced

economies like the U.S. (Attanasio and Davis, 1996, Hayashi, Altonji, and Kotlikoff, 1996).

For developing countries as well, there is a large amount of evidence, surveyed in Banerjee

and Duflo (2005), that the representative agent assumption is strongly violated. When that

is the case, analyzing aggregate data may in some cases lead us to miss the most important

effects of financial liberalization, and in others produce estimates that are not informative

about welfare implications for the average individual in the economy (Levchenko, 2005,

Broner and Ventura, 2006). The use of sector-level data therefore enables us to get a

deeper understanding of how financial liberalization affects the typical agent. In the last

2See Barro, Mankiw, and Sala-i-Martin (1995), and Gourinchas and Jeanne (2006).3See Saint-Paul (1992) and Obstfeld (1994).4See Tornell and Velasco (1992) and Quadrini (2005) for models in which capital mobility reduces produc-

tion inefficiencies associated with imperfect property rights or time-inconsistent fiscal policies. In contrast,Tressel and Verdier (2007) suggest that financial liberalization can increase production inefficiencies byexacerbating the misallocation of credit towards politically connected firms.

5See Galí (1994, 1995) for a detailed analysis. Note that in this model, output growth volatility tendsto increase temporarily as an economy transitions from a steady-state with a low level of capital and highmarkups to a steady-state with high level of capital and low markups.

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section of the paper, we demonstrate the importance of the distinction between industry-

level and aggregate effects. In particular, while the change in aggregate growth implied

by our estimates is the same as the industry-level change, aggregate volatility increases by

much less than sector-level volatility due to diversification across sectors.

Second, existing cross-country results are most likely subject to significant endogeneity

and omitted variables problems. The key feature of our empirical approach is the variety of

empirical strategies we pursue in order to obtain reliable estimates. We isolate a number of

financial liberalization episodes using the de jure liberalization indices developed by Kamin-

sky and Schmukler (2008) and compare the growth and volatility of outcomes, such as output

and employment, during the 10 years immediately before and after the liberalization date.

To address the omitted variables problem, the paper employs two difference-in-differences

strategies. The first, more novel to this paper, uses as the control group countries that did

not liberalize in the same period. To overcome a selection on observables problem that could

arise in such an exercise, we develop a propensity score matching procedure to select a suit-

able control group for each liberalizing country. The second approach, a more conventional

one, exploits differences in sector characteristics in the spirit of Rajan and Zingales (1998)

to identify a causal link between liberalization and growth and volatility. As a way to assess

the robustness of our results, we also estimate the relationship between de facto measures

of financial liberalization, such as those used by Kose, Prasad, and Terrones (2003) and

Lane and Milesi-Ferretti (2006) and growth and volatility. In spite of important differences

in the independent variables and specifications, the findings are remarkably similar for the

two types of measures.6

This paper is related to the large literature on the growth and volatility effects of finan-

cial liberalization, surveyed comprehensively by Kose, Prasad, Rogoff, and Wei (2006) and

Henry (2007). Here, we focus on the papers most closely related to ours. While most exist-

ing studies in this literature use cross-country data, Galindo, Micco, and Ordoñez (2002),

and Gupta and Yuan (2006) employ industry-level data and the Rajan and Zingales (1998)

methodology to analyze the effects of financial liberalization on growth. Our paper dif-

fers from these two contributions in several important respects. First, we investigate the

volatility effects of financial liberalization, doing so within the same empirical framework

6The advantage of de jure measures is that they reflect policy levers, and thus results based on themmay have clearer policy implications for reforms that a government might consider. Their disadvantage isthat they may capture quite poorly the actual degree of financial integration, either because the true natureof legal restrictions is mismeasured, or because these restrictions are imperfectly enforced. Nonetheless, weplace more weight on the de jure measures, since the de facto ones represent equilibrium outcomes, and maybe more noisy reflections of policy.

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as the growth effects. This produces a more complete picture of the effects of financial lib-

eralization, and enables us to evaluate its overall impact. Second, while the Rajan-Zingales

methodology makes it possible to identify the differential impact of financial liberalization

across industries, it does not allow one to estimate the overall effect of financial liberal-

ization. This approach is thus of limited usefulness when it comes to policy evaluation of

financial liberalization reforms. By contrast, our paper proposes a methodology to measure

the overall effect. Third, we establish whether or not the effects of financial liberalization

are temporary or permanent. And finally, we use both de jure and de facto measures of

financial liberalization to assess robustness of the results. In particular, de facto measures

have not previously been used in industry-level analysis.7

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 lays

out the empirical methodology and presents the estimating equations. Section 4 presents

the results, and discusses the implications of our sector-level estimates for aggregate growth

and volatility. Section 5 concludes.

2 Data

Industry-level production, employment, investment, and the number of establishments come

from the 2006 UNIDO Industrial Statistics Database. This paper uses the version that

reports data according to the 3-digit ISIC Revision 2 classification for the period 1963—

2003 in the best cases. There are 28 manufacturing sectors, plus the information on total

manufacturing. We use data reported in current U.S. dollars, and convert them into constant

international dollars using the Penn World Tables (Heston, Summers, and Aten, 2002).8

The resulting dataset is an unbalanced panel of 56 countries, but we ensure that for each

country-year we have a minimum of 10 sectors, and that for each country, there are at least

10 years of data.

The data on de jure financial liberalization come from Kaminsky and Schmukler (2008)

(henceforth KS), who provide indices of liberalization in the stock market, the banking

7A small number of studies attempt to measure the effect of financial liberalization by using firm-leveldata for several countries. Henry (2000a, 2000b) finds that stock market liberalizations are associated witha reduction in the cost of capital, followed by an investment boom in a sample of listed firms in 12 emergingmarkets. Also using listed firms, Mitton (2006) finds that firms with stocks that are open to foreign investorsexperience higher growth, greater profitability, and improved efficiency. Alfaro and Charlton (2007) use alarge cross-section of both listed and non-listed firms in 1999 and 2004 to show that international financialintegration fosters the entry of new firms, a finding in line with our industry-level results.

8Using the variable name conventions from the Penn World Tables, this deflation procedure involves mul-tiplying the nominal U.S. dollar value by (100/P )∗ (RGDPL/CGDP ) for output, and (100/P )∗ (KI/CI)∗(RGDPL/CGDP ) for investment to obtain the deflated value.

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system, and freedom of international transactions for 28 countries. Along each of the three

dimensions of liberalization, KS assign a value of 1, 2, or 3 for each country and year, with

3 indicating the most liberalized. They also provide a composite index, which is a mean

of the three subcomponents. As a measure of de facto financial liberalization we use the

gross capital flows as a share of GDP. The gross capital flows are the sum of gross inflows

and gross outflows, obtained from the IMF’s Balance of Payments Statistics. This measure,

which is parallel to the aggregate trade openness (exports plus imports), has been used by

Kose, Prasad, and Terrones (2003), as well as several subsequent papers.9

In order to test for the differential effect of financial liberalization across industries,

we employ the dependence on external finance measure introduced by Rajan and Zingales

(1998). The Rajan and Zingales measure is defined as capital expenditure minus cash flow,

divided by capital expenditure, and is constructed based on U.S. firm-level data. Intuitively,

it is intended to capture the share of investment that must be financed with funds external

to the firm.10 We also make use of the industry-level measure of liquidity needs compiled

by Raddatz (2006), defined as inventories as a share of sales. A sector has a higher need

for liquidity when a smaller fraction of inventory accumulation can be financed by ongoing

cash flow. Additional controls include financial development — private credit as a share of

GDP — sourced from Beck, Demirgüç-Kunt, and Levine (2000), and trade openness at the

industry level constructed by di Giovanni and Levchenko (2007).

Appendix Table A1 lists the countries in the sample and the summary statistics for

growth, volatility, and gross capital flows for each country, as well as the means and standard

deviations for the entire sample. Table A5 in the supplementary web appendix lists the

sectors used in the analysis, along with the values of external finance dependence and

liquidity needs.

3 Empirical Methodology

3.1 Baseline Specification

In the baseline approach to estimating the effects of financial liberalization, we date the

liberalization events in a sample of countries, and then compare outcomes before and after

liberalization. This strategy relies on the de jure indicators compiled by KS to identify

9We check the results by using instead a measure of stocks of gross foreign assets and liabilities fromLane and Milesi-Ferretti (2006). The results are robust to this alternative index of de facto liberalization,and we do not report them to avoid unnecessary repetition.10We use the version of the variable assembled by Klingebiel, Kroszner, and Laeven (2007), in which

industries are classified according to the 3-digit ISIC Revision 2 classification.

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the liberalization episodes. Because we require precise liberalization dates, we must set a

threshold for the KS index, above which the country is considered liberalized, and below

which it is not.11 The resulting set of liberalization dates is listed in Appendix Table A2.

To estimate the effects of financial liberalization on economic outcomes, we use a con-

ventional difference-in-differences model. For each liberalization episode, we compute the

outcome variable, as well as the relevant controls, for the 10-year period before, and the 10-

year period after the liberalization date. Then, for each episode, we identify a control group

of countries from among those that did not liberalize during the 20-year period around the

liberalization date. Using these, we estimate the following set of specifications:

V OLATILITYict = β0POSTt + β1TREATEDct + γXict +∆+ εict (1a)

GROWTHict = β0POSTt + β1TREATEDct + γXict +∆+ εict. (1b)

Here and throughout the paper, c indexes countries, i industries, and t time periods. On the

left-hand side is either the 10-year average growth rate of a variable (GROWTHict), or the

standard deviation of that growth rate calculated over the 10 year span (V OLATILITYict).

Model (1) is the “classic” difference-in-differences specification. The left-hand side variable

is measured in two periods, before and after treatment. Thus, by construction, in this

model t takes on only two values: before liberalization, and after it. The variable POSTt

takes on the value of 0 before the liberalization episode, and 1 after. It is common to both

treated and control observations. Finally, the coefficient of interest β1 is on the variable

TREATEDct, which is a binary indicator for whether a country is liberalized in a given

period. Intuitively, while the familiar Rajan-Zingales-type model uses non-financially inten-

sive sectors as a control group for the financially intensive sectors, this empirical strategy

uses non-liberalizing countries as a control group for the liberalizing country.

The vector of controls Xict contains the beginning-of-period share of the sector in total

output, as well as exports and imports as a share of output in the sector.12 In addition,

Xict includes a measure of financial development (private credit as a share of GDP), and the

interaction between the country’s financial development and the Rajan-Zingales measure

of dependence on external finance. These are meant to control for the well-documented

differential growth effects of financial development. Raddatz (2006) finds that volatility in11Whenever the financial liberalization index used is not binary, an important question is how to define

a financial liberalization event. In the baseline regressions we classify a country as liberalized whenever allthree components of the index — domestic, capital account, and stock market — indicate full liberalization.This approach emphasizes the complementarities between the different financial liberalization reforms.12We use beginning-of-period values rather than period averages for share to avoid inducing a mechanical

correlation with the left-hand side variable: a faster-growing sector will tend to have higher share in thecontemporaneous period.

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a sector responds to financial development differentially depending on its liquidity needs.

In the volatility specifications we thus control for this effect, by including instead the in-

teraction between financial development and liquidity needs. Appendix Table A3 presents

the correlation matrix for the independent variables. Both specifications include a set of

fixed effects ∆. The ability to employ a variety of fixed effects is a major strength of our

empirical approach, as these can potentially control for a wide range of omitted variables.

The use of fixed effects becomes especially powerful in a three-dimensional panel, which

makes it possible to use interacted effects, such as country×sector, or sector×time.The key question is what countries to assign to the control group for each liberalization

episode. This paper pursues two strategies. First, for each episode we use as the control

group all of the countries that did not liberalize around the same time as the liberalizing

country. This procedure can result in a large number of heterogeneous countries constituting

each control group. To refine this procedure one step, we only use OECD countries as

available controls for the OECD liberalizers, and non-OECD countries as possible controls

for the non-OECD liberalizers. The advantage of this approach is that it uses a large amount

of information for what is happening in various non-liberalizing countries around the time of

each liberalization episode. The disadvantage is that besides the coarse OECD/non-OECD

refinement, no attempt is made to use country characteristics in picking the control groups.

Potentially, this can result in the control group countries having very different characteristics

from the treated ones for each episode. Note that the large size of the control groups should

help in this respect, since the country heterogeneity would be averaged out among the large

number of control countries. Also, many of the obvious differences, such as the overall

level of development, that can arise between a treated country and its control, would be

accounted for by the country fixed effects included in the estimation.

Nonetheless, potential selection concerns remain. In order to overcome them, we also

employ a propensity score matching procedure (henceforth PSM) to find a suitable control

group. The supplementary web appendix to this paper describes it in detail. The PSM

procedure seeks to use information on observable characteristics of subjects to estimate a

probability model for being treated. Then, for each instance of a treated observation, it

uses the information on the observables to identify a non-treated observation closest to

the treated one. That non-treated observation then becomes the control group for the

treated one. The first economic applications of the propensity score techniques are due to

Dehejia and Wahba (1999, 2002), while in international economics they were first used by

Persson (2001) and Glick, Guo and Hutchinson (2006). Though it has been applied widely

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in various empirical analyses, it must be kept in mind that the PSM method corrects only

for selection on observables, not unobservables. Furthermore, it can be sensitive to the set

of conditioning variables used to predict propensity scores (see Smith and Todd, 2005).

3.2 Alternative Estimation Strategies

Because the financial liberalization variable varies at the country×time level, in the baselineempirical model we cannot include country×time effects that would capture any other time-varying country characteristics not picked up by the controls. An alternative approach is

to exploit sector-level characteristics in the spirit of Rajan and Zingales (1998) to identify a

causal relationship between financial liberalization and outcomes. We rely on the variation

in the dependence on external finance introduced by Rajan and Zingales (1998), as well as

the liquidity needs measure from Raddatz (2006). In particular, we estimate the following

specifications on the sample of liberalizing countries:

V OLATILITYict = βCHARi ∗ TREATEDct + γXict + δct + δi + εict (2a)

GROWTHict = βCHARi ∗ TREATEDct + γXict + δct + δi + εict, (2b)

where c indexes countries, i industries, and t time periods. Same as above, GROWTHict and

V OLATILITYict are the average growth rates over the 10-year period, and the standard

deviation of the growth rate over the same period, respectively. TREATEDct is defined

identically to the above specification: it is zero except in the post-liberalization period for

the country that liberalized. CHARi refers to the industry characteristic used in estimation.

This characteristic is either the Rajan and Zingales measure of dependence on external

finance, or the Raddatz measure of liquidity needs. Xict is a vector of controls. All of the

specifications include a full set of country×time effects δct, as well as sector effects δi. Thus,in this model we identify the effect of financial liberalization purely from the differential

effects across industries within a country. The Rajan and Zingales-type approach is a

common one in the literature, indeed we are not the first to analyze the growth effects of

financial liberalization with this strategy (though we are the first, to our knowledge, to

address the issue of volatility).

It is important to emphasize the pros and cons of model (1) compared to (2). The

disadvantage of the former is that it may suffer from an omitted variables problem, because

of our inability to include country×time effects. Its main advantage is that it allows usto estimate the direct effect of financial liberalization on the average growth and volatility

across sectors within a country. By contrast, the omitted variables problem is overcome in

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the Rajan-Zingales-type model. However, its key shortcoming is that because it relies solely

on the within-country cross-industry variation, it does not allow the researcher to identify

the magnitude of the overall effect. That is, the growth effect of financial liberalization —

the object of much study using the cross-country regression approach — is subsumed in the

country×time fixed effect.To further check robustness of the results to alternative measures of financial liberaliza-

tion, we estimate an empirical model based on de facto indices rather than de jure ones:

V OLATILITYict = βFINOPENct + γXict +∆+ εict (3a)

GROWTHict = βFINOPENct + γXict +∆+ εict. (3b)

The sample is a non-overlapping panel of 10-year averages, 1970-79, 1980-89, 1990-99, thus

the subscript t refers to decades. The variable of interest, FINOPENct, is the gross capital

flows as a share of GDP (see Kose, Prasad, and Terrones, 2003). Finally, we also consider a

Rajan-Zingales-type difference-in-differences panel specification in which the de facto mea-

sure of financial integration, FINOPENct, is interacted with industry characteristics.

4 Results

4.1 Volatility and Growth

We now discuss the results of estimating the baseline model (1). Table 1 reports the

estimates of the relationship between financial liberalization and volatility of output. The

first four columns use the full control group, while the last four use the PSM group. As

we cannot use country×time effects, we experiment with various configurations of fixedeffects to control for omitted variables. Column 1 presents estimation results with country

fixed effects, while column 2 uses country×sector fixed effects. Column 3 uses country andgroup×time fixed effects, where we define a “group” to be a single liberalizing countryplus all its control countries. The group×time effects control for the time variation in thevariables affecting both the treated and the control countries, such as the changes in the

global conditions. Finally, column 4 uses the country and group×sector fixed effects. Thelatter is the same as using sector fixed effects, but within each individual group (as, for

example, the sector effects may change over time). Because financial liberalization occurs

at country×time level, we cluster the standard errors at country×time level as well, in orderto avoid biasing the standard errors downwards.

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Financial liberalization appears to increase volatility, as the coefficients of interest with

both the full and the PSM control groups are positive and significant in all but one case. The

coefficient is stable across the control groups and fixed effects configurations. It implies that

a financial liberalization event is associated with a rise in the standard deviation of sectoral

growth of 1.5-2.4 percentage points, or about 0.13-0.21 standard deviations of volatility

found in the sample.

Table 2 reports the results of estimating equation (1b), with the average growth rate

of output over the 10-year period as the dependent variable. Once again, the first four

columns use all available countries as control groups, while the last four report the results

with the PSM control group. The columns differ in their use of fixed effects, identically to

the estimates of the volatility effect of financial liberalization in Table 1.

We can see that financial liberalization has a robust positive effect on growth of output

across sectors. This effect is present across all configurations of fixed effects except one.

Using the PSM control group, the coefficient is significant at 1% in all cases. The magnitude

of the effect is large. A financial liberalization, captured by moving the TREATED variable

from 0 to 1, is associated with a sector-level growth rate that is between 1.5 (full control

group) and 3.5 (PSM control group) percentage points higher. This is equivalent to 0.17

and 0.40 of a standard deviation of the 10-year average sector-level growth rate observed in

the sample.

Among the other controls, the most significant one is the initial share in total output,

which has a negative sign in both the volatility and growth specifications. We interpret this

as a standard convergence effect: sectors that are already large and established experience

less growth in the subsequent period. Trade openness and financial development on its

own do not appear to be robustly significant. The Rajan-Zingales term — private credit

interacted with external finance dependence — has a significant impact on growth in most

specifications, as has been extensively documented. By contrast, the interaction between

financial development and the Raddatz measure of liquidity needs does not appear to have

a robust impact on volatility in our sample.

We next discuss the results of the two alternative estimation strategies laid out above.

First, we employ an alternative difference-in-differences model based on sector characteris-

tics, model (2). Results are presented in Table 3. We can see that within countries, sectors

that rely more on external finance tend to grow faster. In addition, sectors that rely on

external finance, as well as sectors with greater liquidity needs tend to become more volatile

as a result of financial liberalization.

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The second alternative strategy, model (3), uses de facto measures of financial liberal-

ization instead of de jure ones. Table 4 reports the results of estimating equation (3a), in

which the dependent variable is the standard deviation of the growth rate of output over

the 10-year period, while Table 5 reports the results of estimating the impact of financial

liberalization on growth, equation (3b). Unless otherwise indicated, we use the same specifi-

cations, controls, and configurations of fixed effects throughout for maximum comparability.

The independent variable of interest, FINOPEN , is the average gross capital flows over

the same 10-year period. Because FINOPEN is measured at country×time level, wecluster the standard errors at the country×time level as well. The first four columns addprogressively more fixed effects.13

FINOPEN has a positive effect on volatility for all configurations of fixed effects,

though the level of significance is at 10% in most specifications. The magnitude of the

impact of FINOPEN on volatility is economically significant. A one standard deviation

change in FINOPEN is associated with a rise in the standard deviation of sector-level

growth rate of 1.6 percentage points, equivalent to a movement of 0.13 standard deviations

of the sectoral volatility in the sample. From Table 5, it is evident that the financial openness

variable also has a positive effect on the growth rate of total output. The magnitude of

the coefficient of interest is economically significant. A one standard deviation change in

de facto financial openness is associated with a 1.3 percentage points increase in the output

growth rate, a change of 0.16 standard deviations.

Finally, columns 5 and 6 of Tables 4 and 5 interact FINOPEN with the Rajan and

Zingales measure of dependence on external finance and the Raddatz measure of liquidity

needs. We include country×time fixed effects, controlling for other changes — such as reforms— that occur at country level and differ across time. Note that this makes it impossible

to estimate the effect of FINOPEN on volatility or growth, but enables us to make a

statement about its differential impact across sectors. Higher levels of FINOPEN increase

volatility more in sectors that depend more on external finance, or with higher liquidity

needs. When it comes to growth effects, it does appear to be the case that more financially

dependent sectors grow faster as a result of liberalization than less financially dependent

13Column 1 includes country, sector, and time effects separately. Column 2 uses instead country andsector×time fixed effects. Column 3 adds country×sector and time effects. Note that in this column,identification comes purely from the time series variation in the variables of interest. Column 4 includescountry×sector and sector×time fixed effects. This is the most stringent possible array of fixed effects (interms of remaining degrees of freedom) that can be included in this specification. The only difference betweenthe volatility and the growth specifications is that equation (3a) interacts private credit with the Raddatzmeasure of liquidity needs, while equation (3b) interacts private credit with the Rajan and Zingales measureof external finance dependence instead. This difference in the control variables does not affect the results.

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sectors. We do not find a significant differential growth effect for sectors with higher liquidity

needs.

In sum, the estimates using the de jure and de facto measures financial integration yield

strikingly similar results. Both reveal that financial liberalization increases both growth

and volatility. Both effects are magnified in sectors that are more dependent of external

finance, suggesting that those sectors are growing faster in part thanks to higher leverage

in the post-liberalization period.14 When it comes to magnitudes, the impact of a de jure

liberalization on both growth and volatility is larger than that of increasing de facto capital

flows by one standard deviation. The two measures of financial liberalization are not directly

comparable, however. It could be, for instance, that a typical de jure episode we analyze is

equivalent to a more than one standard deviation change in de facto openness.

Our difference-in-differences approach that uses the de jure indicators requires a precise

dating of financial liberalization reforms. Because of this, we transform the KS measure

into a binary indicator and thus overlook the gradual nature of the financial liberalization

process. As a robustness check, we used the original KS index in place of FINOPEN in

the panel specification (model 3).15 Table A6 in the supplementary web appendix shows

that our results are robust to this alternative estimation strategy.

4.2 Factor Accumulation vs. Total Factor Productivity Growth

We next investigate the channels through which financial liberalization increases the growth

rate of output. We would like to know whether it is associated with greater entry (the

number of firms). Furthermore, as in a standard growth accounting framework, growth in

total production can come from increased employment, capital accumulation, and growth

in total factor productivity (TFP). We use the standard techniques to construct the capital

stock and a TFP series for each country and sector (see, for example, Hall and Jones, 1999).

The capital stock in each year t is given by Kict = (1− δ)Kict−1 + Iict, where Iict denotes

investment. We take a depreciation rate δ = 0.08, and adopt the standard assumption that

the initial level of capital stock is equal to Iic0/δ. We then follow Jorgenson and Stiroh

14How do our volatility results compare to the existing estimates? The literature using cross-country datahas focused on the volatility of aggregate consumption rather than output. Even for aggregate consumption,the results are inconclusive: while Kose, Prasad, and Terrones (2003) find, paradoxically, that financialintegration increases consumption volatility, Bekaert, Harvey, and Lundblad (2006) find the opposite. Glick,Guo, and Hutchinson (2006) demonstrate that financial integration reduces the likelihood of currency crises.However, these results are not directly comparable to ours, as currency crises are a different object than theyear-on-year volatility studied here.15The original KS index varies from 1 (no liberalization in any dimension) to 3 (full liberalization in all

dimensions).

13

(2000) to compute total factor productivity at the industry level. Log of TFP in year t is

equal to lnTFPict = lnYict−(1−αic) lnLict−αic lnKict, where Yict is the total output, and

Lict is the total employment in the sector. Each sector in each country has its own labor

share αic, computed as the average of the total wage bill divided by value added.16

Table 6 investigates the impact of financial liberalization on each of these components

of overall growth. We estimate equation (1b) with the growth rate in the number of estab-

lishments, employment, capital accumulation, and TFP as dependent variables. For each of

these, we report results with both the full and PSM control groups. All of the specifications

are presented only with country and group×time fixed effects, though the results are robustacross the various fixed effects configurations. Columns 1 and 2 use the growth rate in the

number of establishments as the dependent variable. The evidence here is mixed. While the

full control group sample produces zero effect, when we select the control group with the

PSM procedure, it turns out that the effect of financial liberalization on entry is strongly

positive. Columns 3 and 4 show that the growth rate of sector-level employment increases

significantly with financial liberalization. Columns 5 and 6 investigate the impact of finan-

cial liberalization on capital accumulation. The effect is positive and robustly significant.

Finally, there does not appear to be a robust positive effect of financial liberalization on

TFP. In one of the specifications it is not significant, while in the other there is a positive

and marginally significant coefficient.

We estimated the impact of financial liberalization on the channels for growth in two

additional ways. First, we used the Rajan-Zingales identification strategy in model (2).

Second, we used the de facto financial openness indices and estimated model (3) for each

subcomponent of growth. In addition, we combined the Rajan-Zingales strategy with de

facto indices. The results are reported in Tables A7 and A8 of the supplementary web ap-

pendix to this paper. They confirm our conclusions regarding the channels for the growth

impact of financial liberalization: there is a robust effect on employment and capital ac-

cumulation, and suggestive evidence of increased entry. However, by and large there is no

robust impact of financial liberalization on TFP growth.17

16Alternatively, we applied to all countries the labor share in sector i in the U.S., or the average laborshare in sector i across all countries in the sample. We also used labor productivity (value added per worker)instead of TFP. The results were unchanged.17We do not report here the decomposition of the volatility results into channels as we did with the growth

results. While in growth accounting the growth rates of each component of the production function add upto the total, the volatilities of the subcomponents do not add up to the volatility of the total because ofthe covariances among the subcomponents. Thus, it is not as informative to report the effect of financialliberalization on each subcomponent, and may be misleading as to what is responsible for the overall effectif the covariances are also changing. Results are nevertheless available upon request.

14

4.3 Temporary vs. Permanent Effects

This paper uses a variety of empirical strategies to document the effect of financial lib-

eralization on growth, volatility, and the various subcomponents of output at a 10-year

horizon. Going much beyond 10 years would be impractical, as there aren’t many liber-

alization episodes in the sample that occurred more than 10 years before our data ends.

However, we can still investigate whether the magnitude of the effect of financial liberaliza-

tion changes over time. This will allow us to establish whether the impact of liberalization

on various outcomes is short-lived, or has a chance to be long-lasting.

In this section, we break the post-liberalization periods into 3-year intervals: 0-2, 3-5,

6-8, and 9-11 years, and use the difference-in-differences model (1) with the PSM control

group to estimate the treatment effect (β1) for each 3-year period after liberalization. Ex-

amining these coefficients will tell us at which lag the effect of financial liberalization is

at its strongest. Figure 2 presents the results. It plots β1 over time, along with the 90%

confidence intervals.

The left panel of Figure 2 presents the timing of the growth effects. It is clear that

the positive effect of financial liberalization occurs early in the sample: the first 6 years.

At longer lags, the effect of financial liberalization on growth becomes muted and not

statistically significant. The time pattern also indicates that the growth effect in the post-

liberalization period is highly non-stationary: growth rises on impact, accelerates further

3 to 5 years after liberalization, and then decelerates to reach zero at the end of the 12

year period. An interesting question is how much of the increase in growth volatility within

10 years found in Section 4.1 is due to the non-stationarity of the growth transition. To

measure this, we compute the increase in growth volatility implied by the time evolution of

the growth effects. We find that it amounts to 1.8%, a figure only slightly lower than the

average post liberalization effect for volatility in the 10 year window presented in Table 1

(2.22%).

The right panel of Figure 2 presents the timing of the volatility effects. Note that we

measure the impact of financial liberalization on short-run growth volatility within each

3-year interval, abstracting from the impact of change in growth between intervals discussed

above. We find that the growth volatility experiences a sharp increase in the immediate

aftermath of financial liberalization. This effect is reduced over time but remains positive.

In the last interval — 9-11 years — the effect on volatility is equal to 1.7%, though it is not

statistically different from zero (p-value of 15.6%). Therefore, we cannot definitively rule

out the possibility of a permanent increase in short-run volatility on top on the temporary

15

increase in medium-run volatility resulting from the growth transition.

How does financial liberalization affect the subcomponents of total output analyzed in

this paper? Figure 3 presents the timing of the effects for each channel affecting growth.18

Panel 1 presents the treatment effect on the growth of the number of establishments. There

is a positive effect in the short run, same as for the total output.19 Panel 2 presents

the results on employment growth. These mirror the overall output results: a positive

and significant short-run effect, becoming muted at longer lags. The results for capital

accumulation growth are presented in Panel 3. What is interesting here is that the effect

of financial liberalization is both longer-lasting, and increasing over time, until the 9th year

or so after liberalization. Thus, the capital accumulation effect is more persistent than

the other outcomes: since capital apparently adjusts slowly, it takes longer to attain the

full impact. Unlike the output and employment effects, the effect of financial liberalization

on capital accumulation is still positive at the longest lag, but it is not significant due

to substantially widened error bands. Panel 4 presents the TFP chart. Consistent with

the regression results from almost all of our specifications, there is no persistent effect of

financial liberalization on TFP growth. It is only in the first two periods that TFP growth

increases significantly. To see whether there is an effect on the level of TFP at 10 year

horizon, we compound the point estimates for each subsequent three-year interval. We find

a cumulative level effect on TFP close to zero.

Finally, panel 5 considers another outcome, the level of the price-cost margin. It is

defined as follows:

PCM =value of sales−wages− cost of inputs

value of sales,

and is meant to capture the size of markups, and thus the competitiveness of the industry

(see Braun and Raddatz, 2008).20 The effect of financial liberalization on the price-cost

margin is negative and significant, quite pronounced, and appears persistent. We call this

reduction in markups the pro-competitive effect of financial liberalization.

18For the reasons mentioned in footnote 17, we do not report the time evolution of the volatility ofcomponents of output. This figure is available upon request.19The results for the number of firms are not presented for the last period (9-11 years), as the coverage

for the number of firms is more sparse than for other variables, and thus there are not enough observationsto obtain a reliable last period estimate.20The PCM is essentially a measure of profitability, or the flow accrued to owners of capital. Though

imperfect as a measure of markups, it has the advantage of simplicity, and has been widely used in theliterature. It is also highly correlated to other indicators of competitiveness, such as industry concentrationratios (see, e.g., Domowitz, Hubbard, and Petersen, 1986). Furthermore, note that our empirical strategyrelies on the time variation in this index. Thus, to the extent that mismeasurement occurs mainly in thecross-section of countries or industries rather than differentially over time, the results are still informative.

16

The finding of a reduction in the markups can, in part, explain why we find a permanent

effect on the level output without any detectable effects on TFP. Since the presence of

markups introduces a wedge between the marginal product of capital and the rental rate

of capital, their reduction can lead to a higher steady-state level of capital and output, as

shown by Galí (1994, 1995). Such a permanent effect on the level of output is also likely

to result in much larger welfare gains from financial liberalization than the ones implied by

the standard neoclassical model (Gourinchas and Jeanne, 2006).

A general feature of our results is the apparent lack of significant effects of financial

liberalization on total factor productivity growth. These results should be interpreted with

caution, as the construction of TFP may be subject to several measurement biases. First, we

do not have direct information on the use of intermediate inputs in sectoral production. The

direction of the resulting bias is hard to assess since it depends on the change in the use of

intermediate inputs relative to the other factors of production.21 Second, as shown by Hall

(1988), a change in the Solow residual under imperfect competition can reflect both a change

in total factor productivity and a change in markups. Note that a reduction in markups

— suggested by the observed reduction in the price-cost margin following liberalization —

would if anything bias our results in favor of finding a positive TFP effect.22 ,23 Finally,

beyond measurement issues, our results are consistent with the recent findings of Hale and

Long (2007) on the lack of productivity spillovers on domestic firms stemming from foreign

direct investment flows.

4.4 Aggregation

Armed with point estimates of how financial liberalization changes sector-level growth and

volatility, we can now calculate what these imply for the aggregate economy. In a country

21 In particular, the fact that a large number of industrial sectors produce intermediate inputs and haveexperienced higher growth following liberalization — possibly suggesting a higher demand for intermediatesfrom other industries — is not directly informative of the direction of the bias in measured TFP growth.An alternative method is to derive total factor productivity growth from value added instead of output.

This approach has the advantage of controlling for the role intermediate inputs but it requires separabilitybetween the value added production function and intermediate inputs, a condition generally not met inindustry-level data (see Jorgenson et al., 1987). We nevertheless computed an alternative measure of TFPbased on value added and the results were unchanged.22Using a fully specified model, Jaimovich (2007) shows that “true” TFP growth (z) is related to the

change in the markups (µ) and the Solow residual (SR) as follows:

z = SR+ µ.

Since our measure of TFP is the Solow residual, a reduction in markups — negative µ — implies that the“true” change in TFP is actually lower than our estimates, not higher.23See Hsieh and Klenow (2007) for a comprehensive analysis of the effect of distortions on sectoral TFP

in China and India.

17

comprised of sectors i = 1, ..., I, denote the level of an aggregate variable by upper case Y A,

and its growth rate by lower case yA. The aggregate growth rate can be written as:

yA =IX

i=1

siyi (4)

where si is the share of sector i in the overall level of Y A in the country, and yi is the growth

rate of this variable in sector i. This paper estimates the change in sector-level growth rate,

∆y, that comes as a result of financial liberalization. The change in the aggregate growth

rate could be obtained from (4) in a straightforward manner:

∆yA =IX

i=1

si∆y = ∆y. (5)

That is, if in each sector the growth rate of a variable increases by ∆y, then the aggregate

growth of that variable will rise by the same amount.

Note that this expression applies to all variables we analyzed: output, employment,

capital stock, and TFP. To compute the change in aggregate TFP in the same way as we

compute changes in output, employment, and capital requires two additional simplifying

assumptions. First, we must assume there is a Cobb-Douglas aggregate production function

Yt = AtKαt L

1−αt . Second, we must assume a time-invariant share of each industry in total

output, total capital and total employment. This assumption rules out composition effects.

In the presence of composition effects, aggregate TFP can increase purely from expansion

of high-TFP sectors and contraction of low-TFP sectors, without any change in TFP at

individual sector level. In order to assess the empirical relevance of this mechanism, we

re-estimated the baseline model while allowing financial liberalization to affect growth in

high-TFP sectors differently from growth in low-TFP sectors. We found no evidence of

composition effects due to financial liberalization: sectors with higher than average initial

TFP do not appear to grow systematically faster than sectors with low initial TFP after

liberalization (estimation results are available upon request).24

24The first assumption yields the level of aggregate TFP as a function of sector-level TFPs:

At =Yt

Kαt L

1−αt

=Xi

Ait

µKit

Kt

¶α µLitLt

¶1−α.

The second assumption leads to the change in the growth rate of aggregate TFP equal to:

∆aAt =dAt

At=Xi

dAit

At

µKit

Kt

¶α µLitLt

¶1−α=

Xi

dAit

Ait

YitYt

=Xi

si∆at = ∆at,

same as equation (5).

18

The empirical analysis above produces the point estimate c∆y of the change in sectorlevel average growth rate in the 10 years following financial liberalization. From (5) we

immediately get d∆yA = c∆y. By the same argument, the standard errors of the aggregategrowth effects are simply equal to the standard errors of the sectoral growth estimates for

each variable of interest. Panel A of Table 7 reports the estimated impact of financial liber-

alization on aggregate growth rates of the variables used in the analysis. Not surprisingly,

they correspond to the values reported in Tables 2 and 6 above.

Next, we evaluate the long-run impact of financial liberalization. Section 4.3 establishes

that there is no long-run impact on growth. However, the temporary growth impact over

the first 10 years following liberalization compounds to yield a long-run level effect. Since

the growth effects of liberalization do not persist after 10 years, the permanent level effect

results from compounding each sectoral growth effect over 10 years: 1+∆Y A∞ = (1+∆y)10.

Hence we set the estimated impact to be [∆Y A∞ = (1 + c∆y)10 − 1. Panel C of Table 7

reports the results. Given the point estimates, we compute the standard errors using the

delta method.25 In addition, in order to assess whether this long-run impact is statistically

different from zero, the t−test is not sufficient due to the non-linear transformation ofthe regression estimates. Therefore, the statistical significance of the long-run level effect

reported in Table 7 comes from a Wald test of this non-linear relationship. We can see

that while the growth effect is confined to the first decade after liberalization, its estimated

long-run level effect is still substantial. The level of aggregate output increases by 28%,

behind a rise of 25.5% in employment, and of 48% in the capital stock.26

When it comes to volatility, equation (4) implies that the change in the standard devi-

ation of aggregate output is equal to:

∆σA =

vuut IXi=1

s2i∆σ2 =√h∆σ,

where ∆σ is the impact of financial liberalization on sector level volatility, and h ≡PI

i=1 s2i

25Assuming c∆y is close enough to its true value, we use the following first order Taylor approximation:

[∆Y A∞ −∆Y A∞ ' 10(1 +∆y)9 · (c∆y −∆y).

Therefore, E([∆Y A∞) ' ∆Y A∞ and

qV ar([∆Y A∞) ' 10(1 + c∆y)9

pbε2 where bε is the estimated standard errorof c∆y.

26We choose to report the long-run level effect based on the estimates from the baseline 10-year regressionsin section 4.1, rather than the 3-year interval regressions in section 4.3. This choice is dictated primarily bythe substantial difficulty in computing the standard errors of the level effect estimates based on the 3-yearinterval regressions. The two methods produce very similar point estimates, however, that are well withinthe confidence interval for the level effects reported in Table 7.

19

is the Herfindahl of production shares in the economy.27 In contrast to the growth increase,

the change in sector-level volatility is moderated by the Herfindahl index of production

shares in the economy. Thus, for any given change in sector-level volatility, the increase in

aggregate volatility is much lower. For instance, the median value of h in our sample is 0.087,

leading to the change in the aggregate volatility equal to about one third of the magnitude

of the change in sector-level volatility: ∆σA = 0.29 ∗∆σ. Panel B of Table 7 reports thechange in aggregate volatility implied by the sector-level volatility estimates in this paper.

While the standard deviation of volatility at sector level is predicted to rise by 2.2% in

the decade following liberalization, the median economy’s aggregate volatility rises by only

0.7% due to diversification across sectors. This effect is quite minor: the average standard

deviation of aggregate output among the countries in our sample is 8.3%. In contrast to

the growth effect above, the volatility impact will vary depending on a country’s level of

diversification across sectors. To get a sense of the variation in the volatility impact across

countries, Table 7 also reports the change in aggregate volatility predicted for countries in

the 25th and the 75th percentiles of the diversification distribution (countries with lower

Herfindahls are more diversified). We can see that the impact does not differ too much,

ranging from 0.6% for the more diversified country to 0.9% for the less diversified one.

The contrast between the aggregation of the growth and the volatility estimates yields an

interesting conclusion. While at the sector level the growth and volatility effects appear

similar in magnitude, the aggregate growth effect is on average three times larger than the

aggregate volatility effect.

5 Conclusion

It is often argued, both theoretically and empirically, that financial liberalization should

affect economic growth. At the same time, claims that financial liberalization increases

volatility are made just as often. This paper uses a large panel of industry-level data

to analyze both growth and volatility effects within the same empirical framework. A

key strength of our approach is the number of alternative strategies we use to estimate

these relationships. We employ a variety of difference-in-differences estimates, and use

both de jure and de facto measures of liberalization. We exploit sector characteristics, use

non-liberalizing countries as controls, develop a propensity score matching procedure to

overcome selection on observables, and use a variety of fixed effects throughout to control

27This assumes that liberalization does not have a significant effect on the covariances between the sectorsin the economy, which appears to be the case in our data.

20

for omitted variables. What is remarkable is that the conclusions are virtually the same

across all empirical strategies.

There is strong evidence that financial liberalization increases both growth and volatility

of output. Those effects are not long-lasting: they typically vanish after 6 years. When

it comes to channels, we find that financial liberalization is accompanied by an increase

in the growth of employment and capital formation. Furthermore, liberalization exerts

procompetitive pressures on the product market: there is a transitory increase in the entry

of firms and a permanent drop in the price to cost margin. By contrast, the growth rate of

TFP does not appear to be affected by liberalization.

Thus, both growth and volatility increase as a result of financial liberalization, though

admittedly the significance of the volatility results is uniformly lower. Can we say something

about the net welfare impact? While a complete treatment of the welfare question would

require a fully specified growth model and is therefore outside the scope of this paper, it

is relatively easy to pin down the direction of the net effect. Lucas (1987) shows that the

welfare benefits of removing all of the U.S. business cycle volatility are minuscule — about

0.05% of consumption. By the same logic, the adverse welfare impact of higher volatility

due to liberalization is quite small. In fact, the 3 percentage point reduction in consumption

volatility Lucas considered is actually higher than the 1.5-2.5 percentage point increase in

output volatility implied by the estimates in this paper. Even for this small adverse effect

there are several mitigating factors. First, the estimates in this paper are for the volatility

of output, not consumption. If agents can self-insure by smoothing intertemporally, the

implied welfare effect is lower. Second, the estimates are at sector level. Thus, if there

is any amount of risk sharing across sectors, the adverse welfare impact would be reduced

further. On the other hand, though the growth effect of financial liberalization is estimated

to be temporary, it still translates into a permanent level effect. The results presented in

this paper therefore imply that the welfare impact of financial liberalization is positive.

References

[1] Alfaro, Laura, and Andrew Charlton, 2007, “International Financial Integration andEntrepreneurship,” NBER Working Paper 13118.

[2] Attanasio, Orazio, and Steven Davis, 1996, “Relative Wage Movements and the Dis-tribution of Consumption,” Journal of Political Economy, 104(6), 1227-1262.

[3] Banerjee, Abhijit, and Esther Duflo, 2005, “Growth Theory through the Lens of De-velopment Economics,” Handbook of Economic Growth, Volume 1A, Steven Durlaufand Philippe Aghion, editors, Elsevier Science Ltd.-North Holland.

21

[4] Barro, Robert J., N. Gregory Mankiw, and Xavier Sala-i-Martin, 1995, “Capital Mo-bility in Neoclassical Models of Growth,” American Economic Review, 85, 103-15.

[5] Beck, Thorsten, Aslí Demirgüç-Kunt, and Ross Levine, 2000, “A New Database onFinancial Development and Structure,” World Bank Economic Review, 14, 597-605.

[6] Bekaert, Geert, Campbell Harvey, and Christian Lundblad, 2006, “Growth Volatilityand Financial Liberalization,” Journal of International Money and Finance, 25, 370-403.

[7] Braun, Matias, and Claudio Raddatz, 2008, “The Politics of Financial Development:Evidence from Trade Liberalization,” Journal of Finance, 63, 1469-1508.

[8] Broner, Fernando, and Jaume Ventura, 2006, “Globalization and Risk Sharing,”mimeo, CREI-UPF.

[9] Dehejia, Rajeev, and Sadek Wahba, 1999, “Causal Effects in Non-Experimental Stud-ies: Re-Evaluating the Evaluation of Training Programs,” Journal of the AmericanStatistical Association, 94, 1053-1062.

[10] di Giovanni, Julian, and Andrei A. Levchenko, 2007, “Trade Openness and Volatility,”forthcoming, Review of Economics and Statistics.

[11] Domowitz, Ian, R. Glenn Hubbard, and Bruce Petersen, 1986, “Business Cycles andthe Relationship Between Concentration and Price-Cost Margins,” Rand Journal ofEconomics, 17, 1-17.

[12] Galí, Jordi, 1994, “Monopolistic Competition, Endogenous Markups, and Growth,”European Economic Review, 38, 748-756.

[13] Galí, Jordi, 1995, “Product Diversity, Endogenous Markups, and Development Traps,”Journal of Monetary Economics, 36, 39-63.

[14] Galindo, Arturo, Alejandro Micco, and Guillermo Ordoñez, 2002, “Financial Liberal-ization: Does It Pay to Join the Party?” Economia, 3, 231-252.

[15] Glick, Reuven, Xueyan Guo, and Michael Hutchinson, 2006, “Currency Crises, CapitalAccount Liberalization, and Selection Bias,” Review of Economics and Statistics, 88,698-714.

[16] Gourinchas, Pierre-Olivier, and Olivier Jeanne, 2006, “The Elusive Gains from Inter-national Financial Integration,” Review of Economic Studies, 73, 715-741.

[17] Gupta, Nandini, and Kathy Yuan, 2006, “On the Growth Effects of Stock MarketLiberalizations,” mimeo, Indiana University and University of Michigan.

[18] Hale, Galina, and Cheryl Long, 2007, “Is There Evidence of FDI Spillovers on ChineseFirms’ Productivity and Innovation?” mimeo, Federal Reserve Bank of San Francisco.

[19] Hall, Robert, and Chad Jones, 1999, “Why Do Some Countries Produce So Much MoreOutput per Worker then Others,” Quarterly Journal of Economics, 114, 83-116.

[20] Hayashi, Fumio, Joseph Altonji, and Laurence Kotlikoff, 1996, “Risk-Sharing betweenand within Families,” Econometrica, 64(2), 261-294.

[21] Henry, Peter Blair, 2000a. “Stock Market Liberalization, Economic Reform, andEmerging Market Equity Prices,” Journal of Finance, 55(2), 529-564.

[22] Henry, Peter Blair, 2000b. “Do Stock Market Liberalizations Cause InvestmentBooms?” Journal of Financial Economics, 58(1-2), 301-334.

22

[23] Henry, Peter Blair, 2007, “Capital Account Liberalization: Theory, Evidence, andSpeculation,” Journal of Economic Literature, 45, 887-935.

[24] Heston, Alan, Robert Summers, and Bettina Aten, 2002, “Penn World Table Version6.1,” Center for International Comparisons at the University of Pennsylvania (CICUP).

[25] Hsieh, Chang-Tai, and Peter Klenow, 2007, “Misallocation and Manufacturing TFP inChina and India,” mimeo, UC Berkeley.

[26] Jaimovich, Nir, 2007, “Firm Dynamics and Markup Variations: Implications for Multi-ple Equilibria and Endogenous Economic Fluctuations,” Journal of Economic Theory,137, 300-325.

[27] Jorgenson, Dale W., Frank M. Gollop, and Barbara M. Fraumeni, 1987, Productivityand U.S. Economic Growth, Harvard University Press, Cambridge, MA.

[28] Jorgenson Dale W., and Kevin J. Stiroh, 2000, “U.S. Economic Growth at the IndustryLevel,” American Economic Review, 90(2), 161-167.

[29] Kaminsky, Graciela, and Sergio Schmukler, 2008, “Short-Run Pain, Long-Run Gain:The Effects of Financial Liberalization,” Review of Finance, 12, 253-292.

[30] Klingebiel, Daniela, Randall Kroszner, and Luc Laeven, 2007, “Financial Crises, Finan-cial Dependence, and Industry Growth,” Journal of Financial Economics, 84, 187-228.

[31] Kose, M. Ayhan, Eswar Prasad, Kenneth Rogoff, and Shang-Jin Wei, 2006, “FinancialGlobalization, A Reappraisal,” IMF Working Paper 06/189.

[32] Kose, M. Ayhan, Eswar Prasad, and Marco Terrones, 2003, “Financial Integration andMacroeconomic Volatility,” IMF Staff Papers, 50, Special Issue, 119-142.

[33] Lane, Philip, and Gian Maria Milesi-Ferretti, 2006, “The External Wealth of NationsMark II: Revised and Extended Estimates of Foreign Assets and Liabilities, 1970-2004,”IMF Working Paper, 06/69.

[34] Levchenko, Andrei A., 2005, “Financial Liberalization and Consumption Volatility inDeveloping Countries,” IMF Staff Papers, 52(2), 237-259.

[35] Lucas, Robert E. Jr., 1987, Models of Business Cycles, New York: Basil Blackwell.

[36] Mitton, Todd, 2006, “Stock Market Liberalization and Operating Performance at theFirm Level,” Journal of Financial Economics, 81(3), 625-647.

[37] Obstfeld, Maurice, 1994, “Risk-Taking, Global Diversification, and Growth,” AmericanEconomic Review, 84(5), 1310-29.

[38] Persson, Torsten, 2001, “Currency Unions and Trade: How Large is the TreatmentEffect?” Economic Policy (October), 435-448.

[39] Quadrini, Vincenzo, 2005, “Policy Commitment and The Welfare Gains From CapitalMarket Liberalization,” European Economic Review, 49(8), 1927-1951.

[40] Raddatz, Claudio, 2006, “Liquidity Needs and Vulnerability to Financial Underdevel-opment,” Journal of Financial Economics, 80, 677-722.

[41] Rajan, Raghuram, and Luigi Zingales, 1998, “Financial Dependence and Growth,”American Economic Review, 88(3), 559-86.

[42] Saint-Paul, Gilles, 1992, “Technological Choice, Financial Markets and Economic De-velopment,” European Economic Review, 36, 763-781.

23

[43] Smith, Jeffrey and Petra Todd, 2005, “Does Matching Overcome LaLonde’s Critiqueof Nonexperimental Estimators?” Journal of Econometrics, 125(1-2), 305-353.

[44] Tornell, Aaron, and Andres Velasco, 1992, “The Tragedy of the Commons and Eco-nomic Growth, Why Does Capital Flow from Poor to Rich Countries?,” Journal ofPolitical Economy, 100, 1208-1231.

[45] Tressel, Thierry, and Thierry Verdier, 2007, “Does Financial Globalization DisciplinePolitically Connected Firms,” mimeo, IMF.

[46] United Nations Industrial Development Organization, 2006, “Industrial StatisticsDatabase,” CD-ROM.

24

Figure 1: Worldwide Financial Liberalization Trends

1.5

22.

53

De

Jure

Lib

eral

izat

ion

.05

.1.1

5.2

.25

Wor

ld G

ross

Cap

ital F

low

s/G

DP

1975 1980 1985 1990 1995 2000

World Gross Capital Flows/GDP De Jure Liberalization

Notes: The World Gross Capital Flows/GDP are the sum of the gross capital flows across countries, divided by world GDP, in each year. Source: IMF Balance of Payments Statistics and World Bank’s World Development Indicators. De Jure Liberalization is the average composite index of financial liberalization across countries in each year. The index ranges from 1 (least liberalized) to 3 (fully liberalized). Source: Kaminsky and Schmukler, (2008).

Figure 2: The Time Evolution of the Growth and Volatility Effects of Financial Liberalization

-.02

0.0

2.0

4.0

6C

oeffi

cien

t

0 2 4 6 8 10Years

Output Growth

-.02

0.0

2.0

4.0

6C

oeffi

cien

t

0 2 4 6 8 10Years

Output Growth Volatility

Notes: This figure depicts the treatment effect of financial liberalization for the outcome variables over time. The solid line is the coefficient on the TREATED dummy variable in the years 0-2, 3-5, 6-8, and 9-12 after the liberalization episode. Dashed lines represent the 10% significance bands.

25

Figure 3: The Time Evolution of the Effect of Financial Liberalization: Channels

0.0

2.0

4.0

6.0

8C

oeffi

cien

t

0 2 4 6 8 10Years

Number of Establishments

-.02

0.0

2.0

4.0

6C

oeffi

cien

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0 2 4 6 8 10Years

Employment

-.02

0.0

2.0

4.0

6.0

8C

oeffi

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0 2 4 6 8 10Years

Capital Stock

-.1-.0

50

.05

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ffici

ent

0 2 4 6 8 10Years

TFP

-.06

-.04

-.02

0C

oeffi

cien

t

0 2 4 6 8 10Years

Price-Cost Margin

Notes: This figure depicts the treatment effect of financial liberalization for the outcome variables over time. The solid line is the coefficient on the TREATED dummy variable in the years 0-2, 3-5, 6-8, and 9-12 after the liberalization episode. Dashed lines represent the 10% significance bands. All variables are in growth rates with the exception of the price-cost margin which is in level.

26

Table 1: Difference-in-Differences Results Based on Control Countries, Volatility(1) (2) (3) (4) (5) (6) (7) (8)

Dep. Var.: Standard Deviation of the Growth Rate of Output

Treated 0.022** 0.021 0.015** 0.022** 0.022** 0.022** 0.024*** 0.023*[0.009] [0.014] [0.006] [0.009] [0.011] [0.011] [0.006] [0.013]

Post -0.002 -0.001 0.013 -0.001 -0.005 -0.004 0.034*** -0.003[0.007] [0.011] [0.013] [0.007] [0.016] [0.017] [0.012] [0.020]

Exports/Output 0.030*** 0.016 0.030*** 0.016*** 0.021*** 0.016** 0.021*** 0.008[0.005] [0.015] [0.005] [0.005] [0.008] [0.008] [0.008] [0.007]

Imports/Output 0.003 0.000 0.002 0.001 0.003 0.002 0.003 0.002[0.002] [0.002] [0.002] [0.001] [0.002] [0.001] [0.002] [0.002]

Initial Share -0.281*** -0.888*** -0.282*** -0.250*** -0.234*** -0.445* -0.235*** -0.103[0.026] [0.216] [0.026] [0.059] [0.047] [0.259] [0.047] [0.092]

Private Credit -0.016 -0.124** -0.073** -0.069* 0 -0.027 -0.017 -0.017[0.037] [0.062] [0.037] [0.041] [0.092] [0.109] [0.060] [0.118]

Private Credit*Liq.Needs -0.089 0.617 -0.088 0.258* -0.067 0.099 -0.062 0.03[0.057] [0.397] [0.057] [0.131] [0.072] [0.383] [0.072] [0.245]

Country FE yes no yes yes yes no yes yesCountry*Sector FE no yes no no no yes no noGroup*Time FE no no yes no no no yes noGroup*Sector FE no no no yes no no no yesControl Group ALL ALL ALL ALL PSM PSM PSM PSMObservations 3789 3789 3789 3789 1738 1738 1738 1738R-squared 0.28 0.71 0.29 0.48 0.3 0.72 0.33 0.57 Notes: Robust standard errors clustered at country*time level in brackets; * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the standard deviation of the growth rate of output during the 10 years immediately before or immediately after an episode of financial liberalization. Treated takes on the value of 1 if a liberalization event took place in a country, and zero otherwise. Post takes on the value of zero before the liberalization event, and 1 after, for all countries irrespective of whether they liberalized. Initial Share is the beginning-of-period share of output in a sector in total manufacturing output. Exports/Output and Imports/Output are the exports and the imports in the sector divided by the total output in the sector. Private Credit is the private credit by banks and other financial institutions as a share of GDP. Liq. Needs is the sector-level measure of liquidity needs. In the first 4 columns the control group consists of all countries (within the group of OECD/non-OECD) that did not liberalize within the 20-year period. In the last four columns the control group is the country selected by the propensity score matching procedure (PSM). All specifications are estimated using OLS, and including the fixed effects specified in the table. Variable definitions and sources are described in detail in the text.

27

Table 2: Difference-in-Differences Results Based on Control Countries, Growth(1) (2) (3) (4) (5) (6) (7) (8)

Dep. Var.: Growth Rate of Output

Treated 0.015* 0.015 0.015** 0.016* 0.034*** 0.035*** 0.025*** 0.035***[0.008] [0.013] [0.006] [0.009] [0.008] [0.013] [0.006] [0.010]

Post -0.012*** -0.011* -0.003 -0.013*** -0.021*** -0.021* -0.007* -0.021**[0.004] [0.006] [0.002] [0.004] [0.007] [0.011] [0.004] [0.009]

Exports/Output -0.004 -0.013** -0.004 -0.004 0.003 -0.004 0.003 0.004[0.004] [0.006] [0.004] [0.003] [0.005] [0.013] [0.005] [0.006]

Imports/Output 0 -0.001 0 0 -0.004* -0.003 -0.005** -0.005*[0.000] [0.002] [0.001] [0.001] [0.002] [0.003] [0.002] [0.003]

Initial Share -0.053*** -1.553*** -0.054*** -0.218*** -0.036 -1.598*** -0.038 -0.191***[0.019] [0.146] [0.019] [0.041] [0.030] [0.205] [0.030] [0.072]

Private Credit 0.026 -0.006 -0.028* 0.039* -0.051 -0.091 0.011 -0.055[0.018] [0.029] [0.017] [0.020] [0.039] [0.058] [0.034] [0.049]

Private Credit*Extern.Fin 0.053*** 0.176*** 0.052*** 0.007 0.059*** 0.182*** 0.059*** 0.053**[0.005] [0.016] [0.005] [0.016] [0.007] [0.027] [0.007] [0.026]

Country FE yes no yes yes yes no yes yesCountry*Sector FE no yes no no no yes no noGroup*Time FE no no yes no no no yes noGroup*Sector FE no no no yes no no no yesControl Group ALL ALL ALL ALL PSM PSM PSM PSMObservations 3799 3799 3799 3799 1738 1738 1738 1738R-squared 0.35 0.75 0.39 0.5 0.43 0.79 0.47 0.63

Notes: Robust standard errors clustered at country*time level in brackets; * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the average growth rate of output during the 10 years immediately before or immediately after an episode of financial liberalization. Treated takes on the value of 1 if a liberalization event took place in a country, and zero otherwise. Post takes on the value of zero before the liberalization event, and 1 after, for all countries irrespective of whether they liberalized. Initial Share is the beginning-of-period share of output in a sector in total manufacturing output. Exports/Output and Imports/Output are the exports and the imports in the sector divided by the total output in the sector. Private Credit is the private credit by banks and other financial institutions as a share of GDP. Extern.Fin. is the sector-level measure of reliance on external finance. In the first 4 columns the control group consists of all countries (within the group of OECD/non-OECD) that did not liberalize within the 20-year period. In the last four columns the control group is the country selected by the propensity score matching procedure (PSM). All specifications are estimated using OLS, and including the fixed effects specified in the table. Variable definitions and sources are described in detail in the text.

28

Table 3: Difference-in-Differences Results Based on Industry Characteristics(1) (2) (3) (4)

Extern.Fin*treated 0.016* 0.024*[0.010] [0.015]

Liq.Needs*treated 0.100 0.158*[0.067] [0.097]

Exports/Output 0.004 0.005 0.007 0.008[0.005] [0.005] [0.011] [0.011]

Imports/Output -0.004** -0.004** -0.001 -0.001[0.002] [0.002] [0.001] [0.001]

Initial Share -0.109* -0.113* -0.332*** -0.329***[0.066] [0.064] [0.105] [0.103]

Private Credit*Extern.Fin 0.005 0.031[0.020] [0.029]

Private Credit*Liq. Needs 0.237 0.316[0.152] [0.202]

Country*Time FE yes yes yes yesSector FE yes yes yes yesObservations 852 852 851 851R-squared 0.55 0.55 0.46 0.46

Growth VolatilityOutput

Notes: Robust standard errors in brackets; * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the average growth rate, or the standard deviation of the growth rate of output during the 10 years immediately before or immediately after an episode of financial liberalization. Treated takes on the value of 1 if a liberalization event took place, and zero otherwise. Private Credit is the private credit by banks and other financial institutions as a share of GDP. Extern.Fin. is the sector-level measure of reliance on external finance. Liq. Needs is the sector-level measure of liquidity needs. Initial Share is the beginning-of-period share of output in a sector in total manufacturing output. Exports/Output and Imports/Output are the exports and the imports in the sector divided by the total output in the sector. All specifications are estimated using OLS, and including country*time and sector fixed effects. Variable definitions and sources are described in detail in the text.

29

Table 4: De Facto Financial Liberalization and Volatility, 10-year Panel Estimates (1) (2) (3) (4) (5) (6)

Dep. Var.: Standard Deviation of the Growth Rate of Output

FINOPEN 0.266** 0.271** 0.269* 0.277*[0.108] [0.109] [0.147] [0.149]

Extern.Fin*FINOPEN 0.164**[0.081]

Liq.Needs*FINOPEN 1.836***[0.598]

Log(Output/Worker) -0.021*** -0.021*** -0.021 -0.023 -0.020*** -0.020***[0.005] [0.005] [0.013] [0.014] [0.005] [0.005]

Initial Share -0.283*** -0.282*** -0.289* -0.335** -0.288*** -0.280***[0.041] [0.042] [0.147] [0.166] [0.042] [0.042]

Exports/Output 0.005 -0.018 -0.145 -0.211 -0.043 -0.039[0.124] [0.120] [0.210] [0.211] [0.128] [0.129]

Imports/Output -0.005 0.000 0.005 0.016 0.000 -0.001[0.025] [0.024] [0.031] [0.031] [0.025] [0.025]

Private Credit 0.007 0.014 -0.067 0.011[0.048] [0.047] [0.080] [0.093]

Private Credit*Liq.Needs -0.08 -0.128 0.38 -0.1 -0.330**[0.160] [0.155] [0.410] [0.525] [0.148]

Private Credit*Extern.Fin -0.006[0.020]

Country FE yes yes no no no noSector FE yes no no no no noTime FE yes no yes no no noCountry*Sector FE no no yes yes no noSector*Time FE no yes no yes yes yesCountry*Time FE no no no no yes yesObservations 3761 3761 3761 3761 3761 3761R-squared 0.39 0.41 0.65 0.66 0.48 0.48 Notes: Robust standard errors in brackets; standard errors are clustered at country-time level in columns (1)-(4); * significant at 10%; ** significant at 5%; *** significant at 1%. The sample is a panel of three decades, 1970-79, 1980-89 and 1990-99; all of the variables are 10-year averages unless otherwise indicated. The dependent variable is the standard deviation of the growth rate of output over the 10-year period. FINOPEN is gross capital flows, defined as the absolute value of total inflows plus the absolute value of total outflows, as a share of GDP. Log(Initial Output/Worker) is the log of output per worker in a sector. Initial Share is the beginning-of-period share of output in a sector in total manufacturing output. Exports/Output and Imports/Output are the exports and the imports in the sector divided by the total output in the sector. Private Credit is the private credit by banks and other financial institutions as a share of GDP. Extern.Fin. is the sector-level measure of reliance on external finance. Liq. Needs is the sector-level measure of liquidity needs. All specifications are estimated using OLS, and including the fixed effects specified in the table. Variable definitions and sources are described in detail in the text.

30

Table 5: De Facto Financial Liberalization and Growth, 10-year Panel Estimates (1) (2) (3) (4) (5) (6)

Dep. Var.: Growth Rate of Output

FINOPEN 0.260*** 0.259*** 0.282*** 0.286***[0.066] [0.066] [0.092] [0.091]

FINOPEN*Extern.Fin 0.187***[0.061]

FINOPEN*Liq.Needs 0.445[0.411]

Log(Initial Output/Worker) -0.015*** -0.014*** -0.036*** -0.036*** -0.013*** -0.013***[0.004] [0.004] [0.008] [0.008] [0.004] [0.004]

Initial Share -0.100*** -0.098*** -0.641*** -0.738*** -0.100*** -0.092***[0.028] [0.029] [0.118] [0.128] [0.029] [0.029]

Exports/Output 0.001 0.002 -0.142 -0.160 0.020 0.036[0.140] [0.137] [0.258] [0.266] [0.124] [0.124]

Imports/Output -0.021 -0.019 0.004 0.009 -0.027 -0.029[0.024] [0.024] [0.035] [0.035] [0.021] [0.021]

Private Credit 0.008 0.01 -0.003 -0.005[0.029] [0.029] [0.042] [0.044]

Private Credit*Extern.Fin 0.036** 0.038** 0.096 0.112 0.017[0.017] [0.017] [0.062] [0.076] [0.015]

Private Credit*Liq.Needs 0.078[0.111]

Country FE yes yes no no no noSector FE yes no no no no noTime FE yes no yes no no noCountry*Sector FE no no yes yes no noSector*Time FE no yes no yes yes yesCountry*Time FE no no no no yes yesObservations 3777 3777 3777 3777 3777 3777R-squared 0.31 0.33 0.57 0.59 0.41 0.41 Notes: Robust standard errors in brackets; standard errors are clustered at country*time level in columns (1)-(4); * significant at 10%; ** significant at 5%; *** significant at 1%. The sample is a panel of three decades, 1970-79, 1980-89 and 1990-99; all of the variables are 10-year averages, unless otherwise indicated. The dependent variable is the growth rate of output. FINOPEN is gross capital flows, defined as the absolute value of total inflows plus the absolute value of total outflows as a share of GDP. Log(Initial Output/Worker) is the log of beginning-of-period output per worker in a sector. Initial Share is the beginning-of-period share of output in a sector in total manufacturing output. Exports/Output and Imports/Output are the exports and the imports in the sector divided by the total output in the sector. Private Credit is the private credit by banks and other financial institutions as a share of GDP. Extern.Fin. is the sector-level measure of reliance on external finance. Liq. Needs is the sector-level measure of liquidity needs. All specifications are estimated using OLS, and including the fixed effects specified in the table. Variable definitions and sources are described in detail in the text.

31

Table 6: Difference-in-Differences Results Based on Control Countries, Channels(1) (2) (3) (4) (5) (6) (7) (8)

Treated 0.006 0.028*** 0.011** 0.023*** 0.018* 0.040*** 0.010* 0.000[0.009] [0.007] [0.005] [0.005] [0.011] [0.011] [0.006] [0.006]

Post -0.084*** -0.018*** -0.054*** -0.050*** 0.023** -0.061*** -0.008 0.002[0.027] [0.007] [0.007] [0.006] [0.011] [0.009] [0.006] [0.008]

Exports/Output -0.003 0.011* 0.007* 0.004 0.003 -0.002 -0.003 0.000[0.003] [0.006] [0.004] [0.004] [0.003] [0.005] [0.003] [0.003]

Imports/Output 0.000 -0.003** -0.001*** -0.003 0.000 0.002 0.000 -0.005***[0.000] [0.001] [0.000] [0.002] [0.000] [0.002] [0.000] [0.001]

Initial Share -0.023 -0.044 -0.049*** -0.031 0.048*** 0.049*** -0.056*** -0.060**[0.014] [0.026] [0.018] [0.019] [0.014] [0.018] [0.013] [0.023]

Private Credit 0.124 0.092 -0.019 -0.018 0.033 0.057 -0.068*** -0.065[0.078] [0.056] [0.015] [0.033] [0.027] [0.068] [0.017] [0.039]

Private Credit*Extern.Fin 0.045*** 0.049*** 0.048*** 0.055*** 0.048*** 0.060*** 0.007*** 0.002[0.005] [0.006] [0.004] [0.005] [0.004] [0.005] [0.002] [0.003]

Country FE yes yes yes yes yes yes yes yesGroup*Time FE yes yes yes yes yes yes yes yesControl Group ALL PSM ALL PSM ALL PSM ALL PSMObservations 2870 1510 3839 1764 3287 1539 3267 1536R-squared 0.42 0.47 0.4 0.48 0.58 0.65 0.24 0.2

Number of Establishments Employment Total factor productivityCapital accumulation

Notes: Robust standard errors clustered at country*time level in brackets; * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the average growth rate of the number of establishments, total employment, capital stock, and TFP during the 10 years immediately before or immediately after an episode of financial liberalization. Treated takes on the value of 1 if a liberalization event took place in a country, and zero otherwise. Post takes on the value of zero before the liberalization event, and 1 after, for all countries irrespective of whether they liberalized. Initial Share is the beginning-of-period share of output in a sector in total manufacturing output. Exports/Output and Imports/Output are the exports and the imports in the sector divided by the total output in the sector. Private Credit is the private credit by banks and other financial institutions as a share of GDP. Extern.Fin. is the sector-level measure of reliance on external finance. In columns (1), (3), (5), and (7) the control group consists of all countries (within the group of OECD/non-OECD) that did not liberalize within the 20-year period. In columns (2), (4), (6) and (8) the control group is the country selected by the propensity score matching procedure (PSM). All specifications are estimated using OLS, and including the fixed effects specified in the table. Variable definitions and sources are described in detail in the text.

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Table 7: The Impact on Aggregate Growth and Aggregate Volatility

Output Capital Employment TFP Herf-25 Herf-50 Herf-75 Output Capital Employment TFP0.024*** 0.040*** 0.023*** 0.000 0.006*** 0.007*** 0.009*** 0.280*** 0.477*** 0.255*** 0.000[0.006] [0.011] [0.005] [0.006] [0.0005] [0.0005] [0.0006] [0.072] [0.151] [0.057] [0.062]

Short Run Impact

Panel B: Aggregate Volatility of Output Panel A: Aggregate Growth Rate

Long run impact

Panel C: Aggregate Level

Notes: Standard errors in brackets (*** significant at 1%). The short run impact corresponds to the estimated changes in the aggregate growth rate/volatility over the 10 years following an episode of financial liberalization. These changes are computed based on estimates of equations (1a) and (1b) and they use the industry-level estimates with the PSM control group and Country and Group*Time fixed effects (Tables 1, 2, and 6). The aggregate volatility impact is reported for three different values for the Herfindahl index: the 25th, 50th, and 75th percentile. The long run impact corresponds to the permanent effect of financial liberalization on the level of aggregate output, employment, capital and TFP. Details of the computation of point estimates and standard errors are described in detail in the text.

33

Appendix Table A1: Country Sample and Summary Statistics

Country Growth St. Dev. Country Growth St. Dev.

Australia 0.017 0.033 0.065 Korea, Rep. 0.105 0.075 0.068Austria 0.020 0.048 0.123 Malawi 0.057 0.117 0.080Bangladesh 0.072 0.211 0.033 Malaysia 0.122 0.078 0.092Canada 0.035 0.062 0.099 Malta 0.044 0.088 0.514Chile 0.051 0.122 0.107 Mauritius 0.051 0.062 0.048Colombia 0.037 0.044 0.044 Mexico 0.042 0.114 0.046Costa Rica 0.011 0.080 0.055 Netherlands 0.014 0.084 0.162Cyprus 0.079 0.097 0.112 New Zealand 0.017 0.049 0.050Denmark 0.006 0.032 0.099 Norway 0.025 0.057 0.101Ecuador 0.066 0.107 0.079 Pakistan 0.078 0.054 0.041Egypt, Arab Rep. 0.045 0.071 0.069 Peru -0.017 0.105 0.069Fiji 0.040 0.103 0.068 Philippines 0.055 0.087 0.082Finland 0.029 0.068 0.102 Poland 0.013 0.119 0.071France 0.022 0.054 0.105 Portugal 0.054 0.089 0.110Germany 0.020 0.048 0.082 Senegal 0.032 0.143 0.072Greece 0.013 0.048 0.041 Singapore 0.110 0.119 0.326Guatemala 0.044 0.120 0.049 South Africa -0.004 0.076 0.051Honduras 0.056 0.058 0.067 Spain 0.032 0.073 0.076Hungary -0.011 0.080 0.078 Sri Lanka 0.086 0.182 0.061Iceland 0.031 0.059 0.051 Sweden 0.017 0.068 0.111India 0.069 0.065 0.017 Syrian Arab Republic 0.104 0.201 0.043Indonesia 0.114 0.066 0.054 Tanzania -0.015 0.109 0.033Ireland 0.052 0.065 0.149 Trinidad and Tobago 0.050 0.137 0.067Israel 0.048 0.121 0.122 Turkey 0.068 0.074 0.030Italy 0.040 0.089 0.084 United Kingdom 0.020 0.083 0.114Jamaica 0.029 0.076 0.062 United States 0.024 0.052 0.053Japan 0.018 0.057 0.047 Uruguay 0.014 0.124 0.080Jordan 0.116 0.154 0.113 Zimbabwe 0.064 0.098 0.033

Mean 0.043 0.088 0.087Standard Deviation 0.033 0.039 0.074

Gross Capital Flows

Total Manufacturing Output Gross Capital Flows

Total Manufacturing Output

Notes: The first two columns report the average growth rate and the standard deviation of the growth rate of total manufacturing output (source: UNIDO database, 2006). The last column reports the average gross capital flows -- absolute value of inflows plus the absolute value of outflows as a share of GDP (source: IMF Balance of Payments Statistics) -- which is used in this paper as a de facto measure of financial integration.

34

Appendix Table A2: Liberalization EpisodesLiberalizing Country Liberalization year Control Country

Canada 1976 DenmarkUnited Kingdom 1981 SpainGermany 1982 JapanUnited States 1982 JapanDenmark 1989 CanadaNorway 1989 CanadaSweden 1989 ChileFinland 1990 CanadaFrance 1990 CanadaIndonesia 1990 Korea, Rep.Ireland 1992 Korea, Rep.Italy 1992 GermanyJapan 1992 GermanyMexico 1992 Korea, Rep.Peru 1992 Korea, Rep.Portugal 1993 Korea, Rep.Spain 1993 GermanyChile 1999 MalaysiaTaiwan Province of China 1999 Malaysia

Notes: This table reports the countries and years of liberalization episodes, defines as the year in which the Kaminsky and Schmukler (2008) index starts taking on the value of 3. The last column reports the control country identified in the propensity score matching procedure, and used in the regressions specifications marked “PSM.” Appendix Table A3: The Correlation Matrix of the Independent Variables

TREATED POST Initial Share Exports/ Output

Imports/ Output

Private Credit

Extern. Fin* Private Credit

TREATED 0.304POST 0.348 0.500Initial Share 0.021 0.083 0.042Exports/Output -0.004 0.039 0.708 0.752Imports/Output 0.005 0.005 -0.055 -0.051 5.051Private Credit 0.110 0.199 0.080 0.099 0.002 0.223Extern. Fin*Private Credit 0.031 0.057 0.074 -0.001 0.108 0.271 0.234

Notes: This table presents the correlation matrix for the independent variables. The off-diagonal elements are correlations. The diagonal elements, in italics, are standard deviations of the variable.

35